Abstract. Removing camera motion blur from a single light field is a
challenging task since it is highly ill-posed inverse problem. The problem
becomes even worse when blur kernel varies spatially due to scene depth
variation and high-order camera motion. In this paper, we propose a
novel algorithm to estimate all blur model variables jointly, including
latent sub-aperture image, camera motion, and scene depth from the
blurred 4D light field. Exploiting multi-view nature of a light field relieves
the inverse property of the optimization by utilizing strong depth cues
and multi-view blur observation. The proposed joint estimation achieves
high quality light field deblurring and depth estimation simultaneously
under arbitrary 6-DOF camera motion and unconstrained scene depth.
Intensive experiment on real and synthetic blurred light field confirms
that the proposed algorithm outperforms the state-of-the-art light field
deblurring and depth estimation methods